DEARLi

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This repo contains the code for our ICCV'25 Findings paper "DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation"

DEARLi 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# DEARLi

**Official implementation of the ICCV 2025 Findings Poster & Oral: **  
**"DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-Supervised Panoptic Segmentation"**

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## Getting Started

For detailed setup instructions -- including installation, dataset preparation, checkpoints, training, and evaluation -- please see:  
[`readmes/GETTING_STARTED.md`](./readmes/GETTING_STARTED.md)

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#### TODO

- [ ] Upload COCO-Objects dataset generation script and panoptic labels  
- [ ] Upload SAM-generated pseudolabels and add instructions to generate them

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## Acknowledgments

This project builds upon several excellent open-source frameworks.  
We thank the respective authors for making their code publicly available:

- [Mask2Former](https://github.com/facebookresearch/Mask2Former)
- [Detectron2](https://github.com/facebookresearch/detectron2)
- [FC-CLIP](https://github.com/bytedance/fc-clip)

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## Citation

If you use **DEARLi** in your research, please cite the following:

```bibtex
@article{martinovic2025dearli,
  title={DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-Supervised Panoptic Segmentation},
  author={Martinović, Ivan and Šarić, Josip and Oršić, Marin and Kristan, Matej and Šegvić, Siniša},
  journal={arXiv preprint arXiv:2507.10118},
  year={2025}
}

DEARLi

DEARLi 최신버젼 다운로드

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DEARLi 최신버젼 다운로드

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